scholarly journals Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long‐term data assimilation

2019 ◽  
Vol 25 (3) ◽  
pp. 938-953 ◽  
Author(s):  
Rong Ge ◽  
Honglin He ◽  
Xiaoli Ren ◽  
Li Zhang ◽  
Guirui Yu ◽  
...  
Keyword(s):  
Steady State ◽  
Data Assimilation ◽  
Turnover Time ◽  
Carbon Turnover ◽  
Ecosystem Carbon ◽  
Steady State Assumption ◽  
State Assumption ◽  
Term Data

scholarly journals Ecosystem carbon transit versus turnover times in response to climate warming and rising atmospheric CO<sub>2</sub> concentration

2018 ◽  
Vol 15 (21) ◽  
pp. 6559-6572 ◽  
Author(s):  
Xingjie Lu ◽  
Ying-Ping Wang ◽  
Yiqi Luo ◽  
Lifen Jiang
Keyword(s):  
Steady State ◽  
Transit Time ◽  
Climate Warming ◽  
Age Structure ◽  
C Sequestration ◽  
Turnover Time ◽  
Atmospheric Co2 ◽  
Ecosystem Carbon ◽  
Subsequent Decrease ◽  
Diagnostic Time

Abstract. Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in the literature, including a commonly used turnover time. However, we know little about how different transit time and turnover time are in representing carbon cycling through multiple compartments under a non-steady state. In this study, we estimate both C turnover time as defined by the conventional stock over flux and mean C transit time as defined by the mean age of C mass leaving the system. We incorporate them into the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate C turnover time and transit time in response to climate warming and rising atmospheric [CO2]. Modelling analysis shows that both C turnover time and transit time increase with climate warming but decrease with rising atmospheric [CO2]. Warming increases C turnover time by 2.4 years and transit time by 11.8 years in 2100 relative to that at steady state in 1901. During the same period, rising atmospheric [CO2] decreases C turnover time by 3.8 years and transit time by 5.5 years. Our analysis shows that 65 % of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35 % increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO2] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the transit time, the turnover time, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of warming and rising atmospheric [CO2] on C diagnostic time and potentially led to deviations in estimating land C sequestration in multi-compartmental ecosystems.

scholarly journals Assessing the Steady‐State Assumption in Water Balance Calculation Across Global Catchments

2020 ◽  
Vol 56 (7) ◽  
Author(s):  
Juntai Han ◽  
Yuting Yang ◽  
Michael L. Roderick ◽  
Tim R. McVicar ◽  
Dawen Yang ◽  
...  
Keyword(s):  
Steady State ◽  
Water Balance ◽  
Balance Calculation ◽  
Steady State Assumption ◽  
State Assumption

Evaluating Steady-State Assumption for Highway Queueing System

1995 ◽  
Vol 121 (2) ◽  
pp. 182-190 ◽  
Author(s):  
Young Tae Son ◽  
Michael J. Cassidy ◽  
Samer M. Modanat
Keyword(s):  
Steady State ◽  
Queueing System ◽  
Steady State Assumption ◽  
State Assumption

scholarly journals Global sensitivity analysis, probabilistic calibration, and predictive assessment for the Data Assimilation Linked Ecosystem Carbon model

2014 ◽  
Vol 7 (5) ◽  
pp. 6893-6948
Author(s):  
C. Safta ◽  
D. Ricciuto ◽  
K. Sargsyan ◽  
B. Debusschere ◽  
H. N. Najm ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Steady State ◽  
Data Assimilation ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Forest Site ◽  
Carbon Cycle Model ◽  
Global Sensitivity ◽  
Predictive Skill ◽  
Ecosystem Carbon

Abstract. In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model. A Global Sensitivity Analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for Quantities of Interest obtained with the Data Assimilation Linked Ecosystem Carbon (DALEC) model. We then employ a Bayesian approach to calibrate the parameters of DALEC using net ecosystem exchange observations at the Harvard Forest site. The calibration exercise is guided by GSA and by Fisher information matrix results that quantify the amount of information carried by the experimental data about specific model parameters. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks. These checks show a better performance for the non-steady state model during the growing season compared to the one employing steady state assumptions. Overall, this study leads to a 40% improvement in the predictive skill of DALEC and highlights the importance of considering correlations in the model parameters as informed by the data.

Sign in / Sign up

Export Citation Format

Share Document